Deep learning has shown promising results in many machine learning applications. In computer vision, the deep learning has been successfully applied to problems, such as object detection and English character recognition. It also showed promising results for speech data where it has been applied for speech recognition and spoken keyword spotting. Generally, the effectiveness of deep neural networks lies in layered representation. The hierarchical feature representation built by the deep neural networks enable compact and precise encoding of the data. A deep learning architecture automatically learns the hierarchy of feature representations where complex features are built on the top of the simple encodings. Higher layers construct more abstract representation of the input data enabling well-generalizing representations.